class_docstr: The feature-store class used to store feature data for GNNs
class_docstr_tok: ["The","feature-store","class","used","to","store","feature","data","for","GNNs"]
code: def _cast_feat_obj_to_backend(feat_obj, backend: str, **kwargs):
      if backend == "numpy":
          if isinstance(feat_obj, (cudf.DataFrame, pd.DataFrame)):
              return _cast_to_numpy_ar(feat_obj.values, **kwargs)
          else:
              return _cast_to_numpy_ar(feat_obj, **kwargs)
      elif backend == "torch":
          if isinstance(feat_obj, (cudf.DataFrame, pd.DataFrame)):
              return _cast_to_torch_tensor(feat_obj.values, **kwargs)
          else:
              return _cast_to_torch_tensor(feat_obj, **kwargs)
      elif backend == "wholegraph":
          return _get_wg_embedding(feat_obj, **kwargs)
  
file_path: cugraph/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py
imports: from __future__ import annotations
  from collections import defaultdict
  from typing import Sequence, Union
  import cudf
  import cupy
  import numpy
  import pandas
  from cugraph.utilities.utils import import_optional, MissingModule
label_desc: Use image node_id 6 for calling the FeatureStore obj's underlying member method code with example usage: obj._cast_feat_obj_to_backend(feat_obj, backend) and returns: _cast_to_numpy_ar, _cast_to_numpy_ar, _cast_to_torch_tensor, _cast_to_torch_tensor, _get_wg_embedding